AI Agents for DevOps: Autonomous Incident Response and On-Call Automation in 2026

“AI agent” has become the default label for almost any automation feature in incident management tooling, which makes it worth being precise about what each named product actually does, versus what it claims. This roundup covers five platforms — PagerDuty, incident.io, Resolve.ai, Xurrent IMR (the product formerly known as Zenduty), and BigPanda — based on what their own sites, documentation, and press releases say as of November 2026. Every performance figure below is a vendor claim or customer testimonial unless otherwise noted; none are independently benchmarked.

At a Glance

Platform	Named AI Agent(s)	Core Capability	Status
PagerDuty	SRE Agent, Scribe Agent, Shift Agent, Insights Agent	Context gathering, diagnostics, remediation, call transcription, scheduling conflict resolution	SRE/Insights Agent: early access (Oct 2025); Scribe/Shift: generally available
incident.io	Investigations (powered by “Nexus”)	Agentic root-cause hypothesis generation, cross-signal reasoning, PR-only remediation	Generally available (per product page)
Resolve.ai	On-Call Agent (“AI SRE”)	Autonomous alert investigation before a human is paged, severity/blast-radius assessment	Generally available, with guardrails for autonomous actions
Xurrent IMR (formerly Zenduty)	Sera (AI SRE teammate), AI Querier, AI Postmortem Generator	Incident investigation, Q&A, automated postmortem drafting, alert correlation	Generally available
BigPanda	L1 Agent, AI Incident Assistant, AI Detection and Response	Alert correlation, L1 triage, surfacing runbooks/root-cause context	Generally available

PagerDuty: An End-to-End Agent Suite

PagerDuty’s October 2025 “Fall ’25” release introduced what it called an end-to-end AI Agent Suite. The SRE Agent learns from related past incidents, automatically surfaces context, recommends and executes diagnostics and remediations, and generates self-updating runbooks — it was in early access at launch, with general availability projected for Q4 2025. Three other named agents round out the suite: Scribe Agent transcribes Zoom calls and chats and generates status updates in Slack or Microsoft Teams (generally available); Shift Agent automatically detects and resolves on-call scheduling conflicts (generally available); and Insights Agent gives context-aware answers from PagerDuty analytics (early access at launch). PagerDuty’s own release states that early adopters reported “up to a double-digit percentage” faster resolution times, and separately claims customers who tried the broader suite resolved incidents “up to 50% faster” — both are vendor-stated figures without published methodology, and the 50% figure applies to the suite overall rather than the SRE Agent specifically.

incident.io: From Human-Assist to Agentic Investigation

incident.io’s approach has visibly evolved. Its original 2024 AI features — an Assistant for natural-language queries over incident history, suggested incident summaries, related-incident matching, and suggested follow-ups — were explicitly framed as “exoskeletons, not robots,” assisting human responders rather than acting autonomously. Its newer Investigations feature, powered by what incident.io calls “Nexus,” goes further: when an incident is declared, it runs an automatic first-pass analysis, producing root-cause hypotheses with confidence levels, a map of affected services, and suggested next steps, then keeps re-assessing as new signals arrive. An adversarial agent reportedly challenges each hypothesis before it reaches the responder, and every hypothesis links back to its underlying sources. Notably, incident.io states Investigations makes no changes on its own in production — the only action it can take is opening a pull request for a human to review and merge. The vendor’s headline claim is getting teams “an order of magnitude faster” from alert to resolution, with no published baseline; a customer (turbopuffer’s CTO) is quoted saying it did in minutes what would otherwise take a human 30–60 minutes — a single testimonial, not a controlled benchmark.

Resolve.ai: An Agent That Investigates Before You’re Paged

Resolve.ai positions its product as an “always-on AI SRE” that joins the on-call rotation itself. Its On-Call Agent autonomously investigates alerts pulled from PagerDuty and other alerting tools, assesses severity and blast radius with supporting evidence, silences noisy alerts, can run GitHub Actions, and routes findings to the right team — ideally before a human is paged at all. Findings are delivered via Slack, Microsoft Teams, a CLI, or the Resolve.ai interface itself. The vendor describes guardrails distinguishing actions agents can take autonomously from those requiring human approval, and says engineer corrections to agent findings are retained as reusable knowledge. Resolve.ai integrates with Datadog, Grafana, Prometheus, and OpenTelemetry-based observability stacks. Customer-stated outcomes include DoorDash’s Shahrooz Ansari saying investigations moved “from hours to minutes” in many incidents, and Coinbase’s Angelo Marletta citing root causes surfaced “72% faster” than their own teams achieved — both single-customer testimonials rather than independently audited metrics, and Resolve.ai does not publish a standardized time-to-resolution benchmark of its own.

Xurrent IMR (Formerly Zenduty): A Notable Industry Consolidation

Worth flagging on its own: Xurrent acquired Zenduty in February 2025, and Zenduty has since been rebranded as Xurrent IMR (Incident Management and Response). The combined product’s named AI features include Sera, described as an “AI SRE teammate” that reads alert payloads, finds similar past incidents, and pulls related postmortems and Slack threads when triggered from an incident channel; an AI Querier for natural-language questions about impacted services; an AI Postmortem Generator that drafts postmortems from logs, metrics, and chat history against a chosen template; an AI Scheduling Assistant; and AI-powered alert correlation. On outcomes, Xurrent cites specific customer figures rather than only generic claims: Pima Community College reports a 49% reduction in MTTR and alerted 34,000 users during outages; PopClub reports critical alerts reaching the service owner in under a minute with P0 acknowledgment in 10–15 minutes; and Bajaj Finserv Health reports monthly reporting time cut from three hours to zero, with incidents acknowledged in under five minutes. These are customer-attributed case-study figures published by the vendor, not independently verified.

BigPanda: Correlation-First AIOps

BigPanda’s framing is closer to classic AIOps than to an autonomous agent acting on infrastructure. Its platform applies AI-driven correlation across observability data, service desk activity, and external dependency data to group related alerts and cut noise, supported by what it calls an “IT Knowledge Graph” unifying observability, ITSM, and organizational data for root-cause analysis. Named AI components include an L1 Agent that analyzes and assigns incidents using AI reasoning for Level-1 triage workflows, an AI Incident Assistant covering natural-language investigation and automated incident-channel collaboration, and AI Detection and Response, which gathers triage insights from service desk activity, change data, and observability sources. Unlike Resolve.ai or PagerDuty’s SRE Agent, BigPanda’s own product page doesn’t describe its agents directly executing remediations — response is framed as surfacing runbooks and historical resolutions and escalating when necessary. BigPanda’s page gives no quantified MTTR or alert-noise-reduction figures; customer quotes are qualitative (IHG cites “much faster” detection time; NYSE describes no longer manually combing through thousands of alerts), with the $250 billion figure on the page describing industry-wide manual IT operations spend, not a BigPanda-specific result.

Trade-offs at a Glance

Platform	Autonomy Level	Human-in-the-Loop Control	Primary Integration Focus
PagerDuty	Executes diagnostics/remediations; GA status still rolling out per agent	Guardrails per agent; human review of runbooks	Native PagerDuty ecosystem, Slack/Teams, MCP server
incident.io	Investigates and hypothesizes; cannot change production directly	Only output is a PR a human must merge	Code, telemetry, deployment history, MCP-connected tools
Resolve.ai	Investigates proactively, before paging a human; can act within defined guardrails	Configurable autonomous-vs-approval-required actions	Datadog, Grafana, Prometheus, OpenTelemetry, PagerDuty
Xurrent IMR	Investigates, drafts postmortems, correlates alerts	Human triggers/reviews most agent outputs	Slack/Teams, service desk, observability sources
BigPanda	Correlates and triages; surfaces (doesn’t execute) remediation	Human executes fixes; agent escalates when needed	CMDB, ITSM, observability, topology data

Bottom Line

Every platform here uses the word “agent,” but they sit at different points on the autonomy spectrum. PagerDuty and Xurrent IMR lean toward broad agent suites covering investigation, communication, and scheduling. incident.io and Resolve.ai both emphasize investigation-first agents that stop short of touching production directly — incident.io by limiting itself to pull requests, Resolve.ai through configurable guardrails. BigPanda remains closer to traditional correlation-and-triage AIOps than to an agent that acts independently. None of the quantified outcome claims in this space — PagerDuty’s “50% faster,” Resolve.ai’s “72% faster,” Xurrent’s “49% MTTR reduction” — come from independent, cross-vendor benchmarks; they’re vendor-reported or single-customer figures, and prospective buyers should ask each vendor for methodology and a trial against their own incident history before trusting the headline number.

FAQ

Can any of these AI agents actually fix a production incident on their own, with no human involved?
Based on each vendor’s own description, no — not fully autonomously in production. incident.io’s Investigations is explicit that its only direct action is opening a pull request for human review. Resolve.ai and PagerDuty’s SRE Agent can execute some diagnostics/remediations, but both describe configurable guardrails requiring human approval for higher-risk actions.

Is Zenduty still a separate product?
No. Xurrent acquired Zenduty in February 2025, and the product has been rebranded as Xurrent IMR (Incident Management and Response); some of Xurrent’s own case studies still refer to the product as “Zenduty (now Xurrent IMR).”

Which platform has the most mature AI feature set as of late 2026?
PagerDuty has the broadest named agent suite (four distinct agents covering diagnostics, transcription, scheduling, and analytics), though several components were still in early access as of its October 2025 announcement. incident.io’s Investigations and Resolve.ai’s On-Call Agent are both narrower but more deeply agentic in their investigation workflow specifically.

Are the vendor-reported speed improvements (e.g., “50% faster,” “72% faster”) independently verified?
No. Every such figure found across these five vendors’ own sites is either a vendor-reported aggregate without published methodology or a single customer testimonial — none point to a third-party or cross-vendor benchmark.

Do these tools replace PagerDuty-style alerting, or sit on top of it?
They mostly sit on top of or alongside existing alerting. Resolve.ai explicitly ingests alerts “from PagerDuty and other alerting tools” rather than replacing them, and BigPanda’s correlation layer is designed to reduce noise from existing monitoring and ITSM tools rather than replace them outright.